云计算
计算机科学
杠杆(统计)
深度学习
荷电状态
多物理
抽象
颗粒过滤器
电池(电)
健康状况
人工智能
分布式计算
机器学习
可靠性工程
工程类
卡尔曼滤波器
功率(物理)
哲学
物理
结构工程
认识论
量子力学
有限元法
操作系统
作者
Dapai Shi,Jingyuan Zhao,Zhenghong Wang,Heng Zhao,Chika Eze,Junbin Wang,Yubo Lian,Andrew Burke
出处
期刊:Energies
[MDPI AG]
日期:2023-04-30
卷期号:16 (9): 3855-3855
被引量:16
摘要
Rechargeable lithium-ion batteries are currently the most viable option for energy storage systems in electric vehicle (EV) applications due to their high specific energy, falling costs, and acceptable cycle life. However, accurately predicting the parameters of complex, nonlinear battery systems remains challenging, given diverse aging mechanisms, cell-to-cell variations, and dynamic operating conditions. The states and parameters of batteries are becoming increasingly important in ubiquitous application scenarios, yet our ability to predict cell performance under realistic conditions remains limited. To address the challenge of modelling and predicting the evolution of multiphysics and multiscale battery systems, this study proposes a cloud-based AI-enhanced framework. The framework aims to achieve practical success in the co-estimation of the state of charge (SOC) and state of health (SOH) during the system’s operational lifetime. Self-supervised transformer neural networks offer new opportunities to learn representations of observational data with multiple levels of abstraction and attention mechanisms. Coupling the cloud-edge computing framework with the versatility of deep learning can leverage the predictive ability of exploiting long-range spatio-temporal dependencies across multiple scales.
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